{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MhoQ0WE77laV"
   },
   "source": [
    "##### Copyright 2018 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2020-09-22T22:12:49.300385Z",
     "iopub.status.busy": "2020-09-22T22:12:49.299701Z",
     "iopub.status.idle": "2020-09-22T22:12:49.302121Z",
     "shell.execute_reply": "2020-09-22T22:12:49.301565Z"
    },
    "id": "_ckMIh7O7s6D"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2020-09-22T22:12:49.305984Z",
     "iopub.status.busy": "2020-09-22T22:12:49.305284Z",
     "iopub.status.idle": "2020-09-22T22:12:49.307789Z",
     "shell.execute_reply": "2020-09-22T22:12:49.307214Z"
    },
    "id": "vasWnqRgy1H4"
   },
   "outputs": [],
   "source": [
    "#@title MIT License\n",
    "#\n",
    "# Copyright (c) 2017 François Chollet\n",
    "#\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a\n",
    "# copy of this software and associated documentation files (the \"Software\"),\n",
    "# to deal in the Software without restriction, including without limitation\n",
    "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
    "# and/or sell copies of the Software, and to permit persons to whom the\n",
    "# Software is furnished to do so, subject to the following conditions:\n",
    "#\n",
    "# The above copyright notice and this permission notice shall be included in\n",
    "# all copies or substantial portions of the Software.\n",
    "#\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
    "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
    "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
    "# DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jYysdyb-CaWM"
   },
   "source": [
    "# 基本分类：对服装图像进行分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S5Uhzt6vVIB2"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td><a target=\"_blank\" href=\"https://tensorflow.google.cn/tutorials/keras/classification\" class=\"\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\" class=\"\">在 TensorFlow.org 上查看</a></td>\n",
    "  <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/keras/classification.ipynb\" class=\"\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\" class=\"\">在 Google Colab 中运行</a></td>\n",
    "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/keras/classification.ipynb\" class=\"\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\" class=\"\">在 GitHub 上查看源代码</a></td>\n",
    "  <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/tutorials/keras/classification.ipynb\" class=\"\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\" class=\"\">下载笔记本</a></td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FbVhjPpzn6BM"
   },
   "source": [
    "本指南将训练一个神经网络模型，对运动鞋和衬衫等服装图像进行分类。即使您不理解所有细节也没关系；这只是对完整 TensorFlow 程序的快速概述，详细内容会在您实际操作的同时进行介绍。\n",
    "\n",
    "本指南使用了 [tf.keras](https://tensorflow.google.cn/guide/keras)，它是 TensorFlow 中用来构建和训练模型的高级 API。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "dzLKpmZICaWN",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9.0\n"
     ]
    }
   ],
   "source": [
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yR0EdgrLCaWR"
   },
   "source": [
    "## 导入 Fashion MNIST 数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DLdCchMdCaWQ"
   },
   "source": [
    "本指南使用 [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) 数据集，该数据集包含 10 个类别的 70,000 个灰度图像。这些图像以低分辨率（28x28 像素）展示了单件衣物，如下所示：\n",
    "\n",
    "<table>\n",
    "  <tr><td>     <img alt=\"Fashion MNIST sprite\" src=\"https://tensorflow.google.cn/images/fashion-mnist-sprite.png\" class=\"\"> </td></tr>\n",
    "  <tr><td align=\"center\">     <b>图 1.</b>  <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST 样本</a>（由 Zalando 提供，MIT 许可）。<br>\n",
    "</td></tr>\n",
    "</table>\n",
    "\n",
    "Fashion MNIST 旨在临时替代经典 [MNIST](http://yann.lecun.com/exdb/mnist/) 数据集，后者常被用作计算机视觉机器学习程序的“Hello, World”。MNIST 数据集包含手写数字（0、1、2 等）的图像，其格式与您将使用的衣物图像的格式相同。\n",
    "\n",
    "本指南使用 Fashion MNIST 来实现多样化，因为它比常规 MNIST 更具挑战性。这两个数据集都相对较小，都用于验证某个算法是否按预期工作。对于代码的测试和调试，它们都是很好的起点。\n",
    "\n",
    "在本指南中，我们使用 60,000 个图像来训练网络，使用 10,000 个图像来评估网络学习对图像分类的准确率。您可以直接从 TensorFlow 访问 Fashion MNIST。请运行以下代码，直接从 TensorFlow 中导入和加载 Fashion MNIST 数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "7MqDQO0KCaWS",
    "tags": []
   },
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t9FDsUlxCaWW"
   },
   "source": [
    "加载数据集会返回四个 NumPy 数组：\n",
    "\n",
    "- `train_images` 和 `train_labels` 数组是*训练集*，即模型用于学习的数据。\n",
    "- *测试集*、`test_images` 和 `test_labels` 数组会被用来对模型进行测试。\n",
    "\n",
    "图像是 28x28 的 NumPy 数组，像素值介于 0 到 255 之间。*标签*是整数数组，介于 0 到 9 之间。这些标签对应于图像所代表的服装*类*：\n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "    <th>标签</th>\n",
    "    <th>类</th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>0</td>\n",
    "    <td>T恤/上衣</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td>1</td>\n",
    "    <td>裤子</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>2</td>\n",
    "    <td>套头衫</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>3</td>\n",
    "    <td>连衣裙</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>4</td>\n",
    "    <td>外套</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>5</td>\n",
    "    <td>凉鞋</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>6</td>\n",
    "    <td>衬衫</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>7</td>\n",
    "    <td>运动鞋</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>8</td>\n",
    "    <td>包</td>\n",
    "  </tr>\n",
    "    <tr>\n",
    "    <td>9</td>\n",
    "    <td>短靴</td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n",
    "每个图像都会被映射到一个标签。由于数据集不包括*类名称*，请将它们存储在下方，供稍后绘制图像时使用："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "IjnLH5S2CaWx",
    "tags": []
   },
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Brm0b_KACaWX"
   },
   "source": [
    "## 浏览数据\n",
    "\n",
    "在训练模型之前，我们先浏览一下数据集的格式。以下代码显示训练集中有 60,000 个图像，每个图像由 28 x 28 的像素表示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "zW5k_xz1CaWX",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cIAcvQqMCaWf"
   },
   "source": [
    "同样，训练集中有 60,000 个标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "TRFYHB2mCaWb",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YSlYxFuRCaWk"
   },
   "source": [
    "每个标签都是一个 0 到 9 之间的整数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "XKnCTHz4CaWg",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TMPI88iZpO2T"
   },
   "source": [
    "测试集中有 10,000 个图像。同样，每个图像都由 28x28 个像素表示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "2KFnYlcwCaWl",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rd0A0Iu0CaWq"
   },
   "source": [
    "测试集包含 10,000 个图像标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "iJmPr5-ACaWn",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ES6uQoLKCaWr"
   },
   "source": [
    "## 预处理数据\n",
    "\n",
    "在训练网络之前，必须对数据进行预处理。如果您检查训练集中的第一个图像，您会看到像素值处于 0 到 255 之间："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "m4VEw8Ud9Quh",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wz7l27Lz9S1P"
   },
   "source": [
    "将这些值缩小至 0 到 1 之间，然后将其馈送到神经网络模型。为此，请将这些值除以 255。请务必以相同的方式对*训练集*和*测试集*进行预处理："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "bW5WzIPlCaWv",
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ee638AlnCaWz"
   },
   "source": [
    "为了验证数据的格式是否正确，以及您是否已准备好构建和训练网络，让我们显示*训练集*中的前 25 个图像，并在每个图像下方显示类名称。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "oZTImqg_CaW1",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5, 5, i + 1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "59veuiEZCaW4"
   },
   "source": [
    "## 构建模型\n",
    "\n",
    "构建神经网络需要先配置模型的层，然后再编译模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gxg1XGm0eOBy"
   },
   "source": [
    "### 设置层\n",
    "\n",
    "神经网络的基本组成部分是*层*。层会从向其馈送的数据中提取表示形式。希望这些表示形式有助于解决手头上的问题。\n",
    "\n",
    "大多数深度学习都包括将简单的层链接在一起。大多数层（如 `tf.keras.layers.Dense`）都具有在训练期间才会学习的参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "9ODch-OFCaW4",
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gut8A_7rCaW6"
   },
   "source": [
    "该网络的第一层 `tf.keras.layers.Flatten` 将图像格式从二维数组（28 x 28 像素）转换成一维数组（28 x 28 = 784 像素）。将该层视为图像中未堆叠的像素行并将其排列起来。该层没有要学习的参数，它只会重新格式化数据。\n",
    "\n",
    "展平像素后，网络会包括两个 `tf.keras.layers.Dense` 层的序列。它们是密集连接或全连接神经层。第一个 `Dense` 层有 128 个节点（或神经元）。第二个（也是最后一个）层会返回一个长度为 10 的 logits 数组。每个节点都包含一个得分，用来表示当前图像属于 10 个类中的哪一类。\n",
    "\n",
    "### 编译模型\n",
    "\n",
    "在准备对模型进行训练之前，还需要再对其进行一些设置。以下内容是在模型的*编译*步骤中添加的：\n",
    "\n",
    "- *损失函数* - 用于测量模型在训练期间的准确率。您会希望最小化此函数，以便将模型“引导”到正确的方向上。\n",
    "- *优化器* - 决定模型如何根据其看到的数据和自身的损失函数进行更新。\n",
    "- *指标* - 用于监控训练和测试步骤。以下示例使用了*准确率*，即被正确分类的图像的比率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "Lhan11blCaW7",
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer=\"adam\",\n",
    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "    metrics=[\"accuracy\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qKF6uW-BCaW-"
   },
   "source": [
    "## 训练模型\n",
    "\n",
    "训练神经网络模型需要执行以下步骤：\n",
    "\n",
    "1. 将训练数据馈送给模型。在本例中，训练数据位于 `train_images` 和 `train_labels` 数组中。\n",
    "2. 模型学习将图像和标签关联起来。\n",
    "3. 要求模型对测试集（在本例中为 `test_images` 数组）进行预测。\n",
    "4. 验证预测是否与 `test_labels` 数组中的标签相匹配。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z4P4zIV7E28Z"
   },
   "source": [
    "### 向模型馈送数据\n",
    "\n",
    "要开始训练，请调用 `model.fit` 方法，这样命名是因为该方法会将模型与训练数据进行“拟合”："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "xvwvpA64CaW_",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5010 - accuracy: 0.8254\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3791 - accuracy: 0.8630\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3377 - accuracy: 0.8763\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3123 - accuracy: 0.8859\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2961 - accuracy: 0.8910\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2791 - accuracy: 0.8966\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2683 - accuracy: 0.9001\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2567 - accuracy: 0.9043\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2478 - accuracy: 0.9078\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2385 - accuracy: 0.9102\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1f2eb55bd30>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W3ZVOhugCaXA"
   },
   "source": [
    "在模型训练期间，会显示损失和准确率指标。此模型在训练数据上的准确率达到了 0.91（或 91%）左右。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wCpr6DGyE28h"
   },
   "source": [
    "### 评估准确率\n",
    "\n",
    "接下来，比较模型在测试数据集上的表现："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "VflXLEeECaXC",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 1s - loss: 0.3434 - accuracy: 0.8826 - 543ms/epoch - 2ms/step\n",
      "\n",
      "Test accuracy: 0.8826000094413757\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yWfgsmVXCaXG"
   },
   "source": [
    "结果表明，模型在测试数据集上的准确率略低于训练数据集。训练准确率和测试准确率之间的差距代表*过拟合*。过拟合是指机器学习模型在新的、以前未曾见过的输入上的表现不如在训练数据上的表现。过拟合的模型会“记住”训练数据集中的噪声和细节，从而对模型在新数据上的表现产生负面影响。有关更多信息，请参阅以下内容：\n",
    "\n",
    "- [演示过拟合](https://tensorflow.google.cn/tutorials/keras/overfit_and_underfit#demonstrate_overfitting)\n",
    "- [避免过拟合的策略](https://tensorflow.google.cn/tutorials/keras/overfit_and_underfit#strategies_to_prevent_overfitting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v-PyD1SYE28q"
   },
   "source": [
    "### 进行预测\n",
    "\n",
    "在模型经过训练后，您可以使用它对一些图像进行预测。模型具有线性输出，即 [logits](https://developers.google.com/machine-learning/glossary#logits)。您可以附加一个 softmax 层，将 logits 转换成更容易理解的概率。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "DnfNA0CrQLSD",
    "tags": []
   },
   "outputs": [],
   "source": [
    "probability_model = tf.keras.Sequential([model, \n",
    "                                         tf.keras.layers.Softmax()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "Gl91RPhdCaXI",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = probability_model.predict(test_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x9Kk1voUCaXJ"
   },
   "source": [
    "在上例中，模型预测了测试集中每个图像的标签。我们来看看第一个预测结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-22T22:13:28.768952Z",
     "iopub.status.busy": "2020-09-22T22:13:28.768178Z",
     "iopub.status.idle": "2020-09-22T22:13:28.770683Z",
     "shell.execute_reply": "2020-09-22T22:13:28.771097Z"
    },
    "id": "3DmJEUinCaXK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.9982241e-07, 5.5403369e-08, 1.8353174e-07, 1.4761626e-07,\n",
       "       2.4380807e-07, 1.9273469e-04, 1.8122660e-06, 6.5027133e-02,\n",
       "       1.7891599e-06, 9.3477517e-01], dtype=float32)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-hw1hgeSCaXN"
   },
   "source": [
    "预测结果是一个包含 10 个数字的数组。它们代表模型对 10 种不同服装中每种服装的“置信度”。您可以看到哪个标签的置信度值最大："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "qsqenuPnCaXO",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "E51yS7iCCaXO"
   },
   "source": [
    "因此，该模型非常确信这个图像是短靴，或 `class_names[9]`。通过检查测试标签发现这个分类是正确的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "Sd7Pgsu6CaXP",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ygh2yYC972ne"
   },
   "source": [
    "您可以将其绘制成图表，看看模型对于全部 10 个类的预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "DvYmmrpIy6Y1",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "    predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "\n",
    "    plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "    if predicted_label == true_label:\n",
    "        color = \"blue\"\n",
    "    else:\n",
    "        color = \"red\"\n",
    "\n",
    "    plt.xlabel(\n",
    "        \"{} {:2.0f}% ({})\".format(\n",
    "            class_names[predicted_label],\n",
    "            100 * np.max(predictions_array),\n",
    "            class_names[true_label],\n",
    "        ),\n",
    "        color=color,\n",
    "    )\n",
    "\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "    predictions_array, true_label = predictions_array, true_label[i]\n",
    "    plt.grid(False)\n",
    "    plt.xticks(range(10))\n",
    "    plt.yticks([])\n",
    "    thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "    plt.ylim([0, 1])\n",
    "    predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "    thisplot[predicted_label].set_color(\"red\")\n",
    "    thisplot[true_label].set_color(\"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zh9yABaME29S"
   },
   "source": [
    "### 验证预测结果\n",
    "\n",
    "在模型经过训练后，您可以使用它对一些图像进行预测。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d4Ov9OFDMmOD"
   },
   "source": [
    "我们来看看第 0 个图像、预测结果和预测数组。正确的预测标签为蓝色，错误的预测标签为红色。数字表示预测标签的百分比（总计为 100）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "HV5jw-5HwSmO",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6, 3))\n",
    "plt.subplot(1, 2, 1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1, 2, 2)\n",
    "plot_value_array(i, predictions[i], test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "Ko-uzOufSCSe",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kgdvGD52CaXR"
   },
   "source": [
    "让我们用模型的预测绘制几张图像。请注意，即使置信度很高，模型也可能出错。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "hQlnbqaw2Qu_",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows * num_cols\n",
    "plt.figure(figsize=(2 * 2 * num_cols, 2 * num_rows))\n",
    "for i in range(num_images):\n",
    "    plt.subplot(num_rows, 2 * num_cols, 2 * i + 1)\n",
    "    plot_image(i, predictions[i], test_labels, test_images)\n",
    "    plt.subplot(num_rows, 2 * num_cols, 2 * i + 2)\n",
    "    plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R32zteKHCaXT"
   },
   "source": [
    "## 使用训练好的模型\n",
    "\n",
    "最后，使用训练好的模型对单个图像进行预测。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "yRJ7JU7JCaXT",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset.\n",
    "img = test_images[1]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vz3bVp21CaXV"
   },
   "source": [
    "`tf.keras` 模型经过了优化，可同时对一个*批*或一组样本进行预测。因此，即便您只使用一个图像，您也需要将其添加到列表中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "lDFh5yF_CaXW",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Add the image to a batch where it's the only member.\n",
    "img = np.expand_dims(img, 0)\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EQ5wLTkcCaXY"
   },
   "source": [
    "现在预测这个图像的正确标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "o_rzNSdrCaXY",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 24ms/step\n",
      "[[4.8876296e-05 9.8614422e-13 9.9840671e-01 4.8548795e-12 1.2351701e-03\n",
      "  2.0851862e-10 3.0921688e-04 1.2246187e-14 5.6615574e-09 1.4430630e-11]]\n"
     ]
    }
   ],
   "source": [
    "predictions_single = probability_model.predict(img)\n",
    "\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "6Ai-cpLjO-3A",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_value_array(1, predictions_single[0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cU1Y2OAMCaXb"
   },
   "source": [
    "`keras.Model.predict` 会返回一组列表，每个列表对应一批数据中的每个图像。在批次中获取对我们（唯一）图像的预测："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "2tRmdq_8CaXb",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions_single[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YFc2HbEVCaXd"
   },
   "source": [
    "该模型会按照预期预测标签。"
   ]
  }
 ],
 "metadata": {
  "colab": {
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